Visual navigation, characterized by its autonomous capabilities, cost effectiveness, and robust resistance to interference, serves as the foundation for vision-based autonomous landing systems. These systems rely heavily on runway instance segmentation, which accurately divides runway areas and provides precise information for unmanned aerial vehicle (UAV) navigation. However, current research primarily focuses on runway detection but lacks relevant runway instance segmentation datasets. To address this research gap, we created the Runway Landing Dataset (RLD), a benchmark dataset that focuses on runway instance segmentation mainly based on X-Plane. To overcome the challenges of large-scale changes and input image angle differences in runway instance segmentation tasks, we propose a vision-based autonomous landing segmentation network (VALNet) that uses band-pass filters, where a Context Enhancement Module (CEM) guides the model to learn adaptive “band” information through heatmaps, while an Orientation Adaptation Module (OAM) of a triple-channel architecture to fully utilize rotation information enhances the model’s ability to capture input image rotation transformations. Extensive experiments on RLD demonstrate that the new method has significantly improved performance. The visualization results further confirm the effectiveness and interpretability of VALNet in the face of large-scale changes and angle differences. This research not only advances the development of runway instance segmentation but also highlights the potential application value of VALNet in vision-based autonomous landing systems. Additionally, RLD is publicly available.

Object detection in remote sensing imagery exhibits difficulties due to complex backgrounds, diverse object scales, and intricate spatial context relationships. Motivated by the problems mentioned above, this paper introduces AeroDetectNet, a novel lightweight and high-precision object detection network custom-designed for aerial remote sensing scenarios, building upon the YOLOv7-tiny algorithm. It enhances performance through four key improvements: the normalized Wasserstein distance for consistent object size sensitivity, the Involution module for reduced background noise, a self-designed RCS-biformer module for better spatial context interpretation, and a self-designed WF-CoT SPPCSP feature pyramid for improved feature map weighting and context capture. Ablation studies conducted on a hybrid dataset composed of three open-source remote sensing datasets (including NWPU VHR-10 remote sensing images, RSOD remote sensing images, and VisDrone UAV images) have demonstrated the effectiveness of four improvements specifically for small-size object detection. Visualizations through Grad-CAM further demonstrate AeroDetectNet’s capacity to extract and focus on key object features. Upon individual testing across three open-source datasets, AeroDetectNet has successfully demonstrated its ability to identify objects in images with a smaller pixel area. Through experimental comparisons with other related studies, the AeroDetectNet achieved a competitive mAP while maintaining fewer model parameters, highlighting its highly accurate and lightweight properties.

Airport detection in remote sensing scenes is a crucial area of research, playing a key role in aircraft blind landing procedures. However, airport detection in remote sensing scenes still faces challenges such as class confusion, poor detection performance on multi-scale objects, and limited dataset availability. To address these issues, this paper proposes a novel airport detection network (TPH-YOLOv5-Air) based on adaptive spatial feature fusion (ASFF). Firstly, we construct an Airport Confusing Object Dataset (ACD) specifically tailored for remote sensing scenarios containing 9501 instances of airport confusion objects. Secondly, building upon the foundation of TPH-YOLOv5++, we adopt the ASFF structure, which not only enhances the feature extraction efficiency but also enriches feature representation. Moreover, an adaptive spatial feature fusion (ASFF) strategy based on adaptive parameter adjustment module (APAM) is proposed, which improves the feature scale invariance and enhances the detection of airports. Finally, experimental results based on the ACD dataset demonstrate that TPH-YOLOv5-Air achieves a mean average precision (mAP) of 49.4%, outperforming TPH-YOLOv5++ by 2% and the original YOLOv5 network by 3.6%. This study contributes to the advancement of airport detection in remote sensing scenes and demonstrates the practical application potential of TPH-YOLOv5-Air in this domain. Visualization and analysis further validate the effectiveness and interpretability of TPH-YOLOv5-Air. The ACD dataset is publicly available.

The rapid development iteration of YOLO models has spurred extensive research into specialized adaptations tailored for remote sensing object detection (RSOD). Typically, these adaptations involve modifying specific YOLO versions to excel in dataset-specific benchmarks. Such efforts primarily focus on enhancing model performance on specific datasets, a trend that, while beneficial for achieving high leaderboard standings, frequently overlooks the critical aspects of generalizability, scalability, and transferability. Consequently, there is a significant gap in the research concerning the development of universally applicable modules that are easy to adapt across various baseline YOLO models—an essential factor for practical deployment and widespread adoption in real-world applications. This study addresses these shortcomings by introducing a novel feature fusion method, Context Enhancement (CE), Weight Adjustment-based Spatial Attention (WA), and Channel Shuffling (CS) — collectively termed CE-WA-CS, specifically designed for RSOD. This research rigorously tests the proposed feature fusion method across various YOLO models, substantiating its versatility and effectiveness in enhancing remote sensing object detection. Notably, in comparative performance evaluations involving several YOLO architectures, integrating our feature fusion method with YOLOv8 emerged as the most balanced in terms of accuracy, computational complexity, and inference speed. This optimal combination has been designated as YOLORemote. YOLORemote not only exemplifies a significant improvement in performance metrics but also demonstrates a practical balance that is crucial for real-time applications.

Small-object detection in remote sensing remains challenging due to low-resolution, weak cues, and complex backgrounds. Although semantic-context enhancement (SCE) methods leverage spatial priors effectively, their effectiveness is often limited by the stage, which contextual features are integrated. When introduced in the backbone, contextual information is subsequently degraded by the neck’s repeated resampling and fusion operations. Conversely, integrating context modeling mechanisms between the neck and the detection head is also suboptimal, as the features have already undergone multiscale mixing and spatial transformations, resulting in weakened semantic representation. To address this issue, we propose YOLO-feature supplement (FeatSup), a context-preserving detector built upon YOLOv11, which supplements the detection head with preserved semantic features via a dedicated bypass path. Specifically, a global−local spatial aggregation (GLSA) module is integrated in the backbone to capture high-fidelity semantic features before scale mixing, while the scale sequence (ScalSeq) fusion module performs effective multiscale feature fusion and delivers the enriched features directly to the small-object detection branch, thereby avoiding the degradation typically caused by repeated resampling and cross-scale operations in the feature aggregation process. Experiments on six public datasets demonstrate that YOLO-FeatSup achieves superior small-object detection accuracy with reduced computational cost, offering an efficient solution for remote sensing applications.

Zero-shot learning (ZSL) endeavors to extend knowledge to novel classes by capitalizing on the semantic overlap across different categories. Contemporary ZSL approaches concentrate on isolating image-specific local features pertinent to attributes and using these to align class semantic vectors. However, existing methods often overlook the integration of global and local representation, a synthesis that could significantly enhance zero-shot recognition accuracy. This paper introduces an innovative ZSL technique, termed ZSL based on the fusion of global representation and local representation (ZGLR). Our approach incorporates a Transformer encoder constructed upon attribute prototypes to extract attribute-level features, which are then mapped to local representation to align with class semantic vectors. Concurrently, we introduce a discriminative semantic-visual mapping network that embeds class semantic vectors into the visual domain, thereby aligning global representation. During training, both global and local representation are optimized in tandem, while in the testing phase, the outcomes from both representational levels are consolidated to bolster classification precision. Practical results from three benchmark ZSL datasets demonstrate the superiority of our put-forward ZGLR solution.